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Cognitive Psychology

Cognitive Psychology

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Cognitive Psychology

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  1. Cognitive Psychology Attention

  2. What do these have in common? You are driving to a lunch date, and accidentally take the route to your job. After you correct your route, as you are driving by the theatre, a red ball chased by a child suddenly appears on the street, and you screech your brakes. You get to the restaurant and try to find your friend, who has flaming red hair. The restaurant is packed, it’s hard to make-out faces, but you can see people’s hair so you look for red hair. When you get to your table your friend asks if you noticed the Star Wars promotion with two costumed people fighting with light sabers. As you talk about important but dull business, your mind keeps drifting to your exciting first date last night. You force yourself not to think about it, but it keeps coming back.

  3. Innatentional Blindness (original experiment) • • Change Blindness (office)

  4. Change Blindness • Counter experiment: • Campus Door Demo: • • Construction door Gradual Change:

  5. Aspects of Attention • Detection. • Filtering and selection. • Search. • Automatic processing. • Concentration.

  6. Architecture The box model: Sensory Store Filter Pattern Recognition Selection STM LTM Input (Environment) Response

  7. Attention • In this model, attention is: • The filter and selection boxes • The arrows. • The special job carried out by each of these boxes according to different theories of attention • (Yes, this is cheating) • In this model attention: • Puts together information from various sources. • Gets information into STM • Works in imagery

  8. Detection • Two kinds of thresholds: • Absolute Threshold: Minimum amount of stimulation required for detection. • Difference Threshold (“Just Noticeable Difference”): Amount of change necessary for two stimuli to be perceived as different.

  9. Detection • Absolute Thresholds: • Vision: One candle, on a mountain, perfectly dark, 30 miles. • Hearing: A watch ticking 20 feet away. • Smell: A single drop of perfume in a three room apartment. • Touch: The wing of a bee on your cheek. • Taste: One teaspoon of sugar in two gallons of water.

  10. Determining Thresholds • How to determine thresholds: • Method of limits: • Ascending: Start with a value below the threshold, increase, ask for detection, increase… At the point a person says “detect,” average that stimulus value with the value from the previous trial. Repeat to estimate threshold. • Descending: Same, but start above threshold and work down. • Combining results from both directions will give you an estimate of the threshold.

  11. Determining Thresholds • How to determine thresholds: • Method of constant stimuli: • Present a series of randomly selected stimulus values, ask for yes/no response for each. The value that’s detected 50% of the time is the threshold. • These methods can be adapted to determine difference thresholds.

  12. Determining Thresholds • We think thresholds work like a step function, but they don’t. They are sigmoid or ogive curves This graph represents an ‘ogive-curve’ and how detection really changes – it is a gradual slope. The threshold is defined as a 50% detection rate. This graph represents a step function. Below the threshold there is 0% detection. Above the threshold, there is 100% detection. This is the way we normally believe our perception to work.

  13. Determining Thresholds • Difference Threshold: • Weber’s Law: K = ΔI / I • K is the Konstant • Δ is the difference • I is the stimulus intensity • The formula states that the threshold for noticing a difference (whether it’s the length of a line or weight of a dumbell) is a constant ration between the ‘old’ / background stimulus and the ‘new’ / target stimulus.

  14. Determining Thresholds • Early Researchers Noticed: Thresholds Shift! These are ogive curves for stimuli of the same intensity but with different signal to noise ratios or payoff matrix • How to get around this problem: A model that accounts for signal to noise ratios and payoff matrixes  Signal Detection Theory

  15. Signal Detection • Can estimate detection (sensitivity) independent of bias. • Two kinds of trials: • Noise alone: Background noise only. • Signal+noise: Background noise with signal. • Two responses from observer: • Detect. • Don’t detect.

  16. Signal Detection:Four Situations

  17. Hits(response “yes” on signal trial) Criterion N S+N Probability density Say “no” Say “yes” Internal response

  18. Correct rejects(response “no” on no-signal trial) Criterion N S+N Probability density Say “no” Say “yes” Internal response

  19. Misses(response “no” on signal trial) Criterion N S+N Probability density Say “no” Say “yes” Internal response

  20. False Alarms(response “yes” on no-signal trial) Criterion N S+N Probability density Say “no” Say “yes” Internal response

  21. Signal Detection:Sensitivity and Bias • We can estimate two parameters from performance in this task: • Sensitivity: Ability to detect. • Good sensitivity = High hit rate + low false alarm rate. • Poor sensitivity = About the same hit and false alarm rates. • Response Bias: Willingness to say you detect. • Can be liberal (too willing) or conservative (not willing enough). • For example, if the true signal to noise ratio is 50% and you have a 75% detection rate, then your response bias is to be too liberal.

  22. Signal Detection:Sensitivity and Bias • Computing sensitivity or d’ (“d-prime”) • Is a measure of performance (like percent correct, or response time) • Typical values are from 0 to 4 (greater than 4 is hard to measure because performance is so close to perfect) • A d-prime value of 1.0 is often defined as threshold.

  23. d’ N S+N Probability density Internal response d-Prime • d-prime is the distance between the N and S+N distributions • d-prime is measure in standard deviations (Z-Scores) • In SDT, one usually assumes the two underlying distributions are normal with equal variance (i.e., both curves have the same standard deviation)

  24. Signal Detection:Sensitivity and Bias • Computing bias: • The criterion is the point above which a person says “detect.” It can be unbiased (the point where the distributions cross; 1.0), liberally biased (< 1.0), or conservatively biased (> 1.0).

  25. Signal Detection:Sensitivity and Bias • Since sensitivity and bias are independent, you can measure the effect of different biases on responding to a particular value for detectability. • Influences on bias: • Instructions (only say “yes” if you’re absolutely sure). • Payoffs (big reward for hits, no penalty for false alarms). • Probability of signal (higher probability leads to more liberal bias).

  26. Signal Detection:Sensitivity and Bias • Receiver operating characteristic (ROC) curves: • For a given detectability value, you can manipulate the hit and false alarm rates. An ROC curve shows the effect of changing bias for that level of detectability.

  27. Very sensitive observer Moderately sensitive observer Zero sensitivity Sample ROC Curves % of Hits

  28. Optimal Performance • Depending on the probability of a signal trial and the payoff matrix, the optimal placement of the criterion will vary. p(N) value (CR) - cost (FA) opt = X p(S) value (H) - cost (M) • You can compare performance to the ideal observer to assess the operator.

  29. Examples of Visual Search Is there a threat? Where’s Waldo?

  30. Search • How do you use attention to locate items in a complicated array? Two kinds of search: Feature Search and Conjunction Search. • Feature search: A single feature allows you to find the item you are searching for. • Find the blue S.

  31. Search

  32. Search

  33. length, width Sagi & Julész 85b; Treisman & Gormican 88 size Treisman & Gelade 80; Healey & Enns 98; Healey & Enns 99x line (blob) orientation Julész & Bergen 83; Sagi & Julész 85a, Wolfe et al. 92; Weigle et al. 2000 closureJulész & Bergen 83 colour (hue) Nagy & Sanchez 90; Healey 96; Bauer et al. 98; Healey & Enns 99 density, contrast Healey & Enns 98; Healey & Enns 99 curvature Treisman & Gormican 88

  34. Search • How do you use attention to locate items in a complicated array? • Conjunction search: You have to combine features to find the item you are searching for. This should take attention and be more difficult (Treisman, 1988). • Find the green T.

  35. Search

  36. Search

  37. T T T T T T O T T T T T T T T T T T T T T T T T T T T T T T T T T T Simple feature search Look for an “O”

  38. T O T T O T T O T T O T O T O O T O T T T O T T O O O T O O T T Simple feature search Look for something red

  39. T O T T O T T O T T T T O O T O O T T O T T T O T O T O O O T O T T O T O Conjunctive feature search Look for something red AND “O”

  40. Conjunctive Search Response Time Simple feature search Number of Stimuli in Display

  41. Feature searches: Don’t require attention (pop-out). No help from location cueing (don’t need it). Conjunction searches: Require attention. Affected by the number of distracters. Helped by cueing the location. Properties of searches:

  42. Pop-Outs in Advertisement

  43. Scan Paths

  44. Feature Conjunction: Attention as Glue ~ The significance between conjunctive and disjunctive searches is that it means that individual features like color and size are loaded pre-attentively (attention is not required), but a conjunctive searchrequires attention to bind the two features to the object to a location in space. You need attention to know an object is both red and large and where it is. ~ The integration may happen in the visual cortex as a result of synchrony, with attention affecting the tuning properties of sensory neurons, and preparing other cognitive processes like working memory.

  45. Attention as GlueKeep your eye on the fixation point below. A screen with colored letters will be briefly flashed. Try to remember as many letters with their colors as you can. +

  46. L S O P Q M Q H U X B T K V Z

  47. Attention as a Glue • What color was the X? • Do you distinctly recall a particular letter being a different color? How did that happen? How did a color in one location get associated with an object in another location? • This is “attention as a glue”